Research On Monitoring Of Stock Index Futures Manipulation

China stock market has already begun to take shape and its internationalization advancement also is greatly accelerating. However, a mature stock market must not only have mature participants and good operation mechanism, but also must have diverse investment tools to meet investor's different need. Stock index futures has become an indispensable investment tool in China financial market. It not only satisfies China capital market urgent need, simultaneously also cultivates organization investors, stabilizes financial market, and promotes China financial market towards internationalization. However, stock index futures, as an investment derivative product, is a"double-edged sword". It can reduce market risk and increase finance system ability to cope with risk, also possibly aggravates destruct capability from financial risk. Therefore, during development of stock index futures, it is necessary to establish monitoring and managing system, to control risk management, and to standardize its operation. At the present, all large stock exchanges are commonly using monitoring methods based on experience and simple statistical division. Although these methods are widely utilized and obtain good effect, they cannot adapt to manipulation behavior and meet the complex analysis demand. A new method based on the data mining technique can carry out more thorough monitoring of stock index futures.Embarking from characteristics of manipulation problems of stock index futures, this article explores a method based on data mining to uncover manipulation behavior with stock index futures, and thus, provides a new support technology and application method to monitor stock index futures.This article first conducted frame research of manipulation recognition of stock index futures, proposed a data - method - index system model for recognition of stock index futures manipulation behavior.Next, starting from major criteria (price, deal, order and fund) for recognition of stock index futures manipulation behavior and analyzing data characteristic of stock index futures, this article has found that, because transaction data has noise, instability, multi-granularity, duality and entity difference, many shortcomings exist when the original data mining method is directly utilized to uncover manipulation behavior during stock index futures transaction. In order to adapt to these characteristics of transaction data, this article modifies the existing data mining technique and proposes three analysis methods when dealing with different transactions: namely (1) stock index futures time series analysis model based on wavelet - BP neural network - ARMAX/GARCH, (2) deal data support vector classification model and (3) order behavior multi-classifier fusion classification model. These new methods have made up the weakness of original method and hence, enhanced the analysis accuracy.In order to adapt to the noise and instability of stock index futures time series, and to overcome the shortcomings of the existing analysis method model, this article modified analysis method for stock index futures time series and put forward an analysis framework based on the wavelet - BP neural network. Based on this model, the analysis method has used the wavelet decomposition, and created a model for different wavelength and finally restructured all wavelet in order to carry out short-term forecast and therefore, to enhance series forecast accuracy.Regarding to the multi-granularity of and analysis demand of stock index futures transaction data, this article proposed a classification method based on support vector machines. Tangent plane solution of support vector machines is established by introducing geometrical solution. Therefore, investors can be classified based on straightforward transaction data.In order to objectively describe duality and entity difference of commission behavior of stock index futures, this article explored and established the multi-classifiers fusion classification model. On one hand, this model utilized decision tree with good performanceas classification method and hence, enabled this method to reflect the dual characteristics of order behavior. On the other hand, this model can use many basic classifiers in the commission data space, fuse their results through the classifier fusions, obtain comprehensive classification result, and therefore, enhance the capability to cover investor attribute space. Considering that entity difference exists in customer data, this article optimized classification fusion using the genetic algorithm. The experiment result has demonstrated that this method is extremely effective.Research results in the article solved some existing problems and the insufficiencies in uncovering manipulation behavior with stock index futures and therefore, opened a broader space for application of the data mining method in manipulation behavior recognition of stock index futures.